Over the years, practitioners of HALT (Highly Accelerated Life Test) and HASS (Highly Accelerated Stress Screen) have wanted a way to predict their product's MTBF (Mean Time Between Failure) from the results of HALT and HASS.
An important design characteristic of any commercial product (particularly electronic products) is the reliability of the product (system). The military broadly defines reliability as “the probability that an item will perform a required function without failure under stated conditions for a stated period of time” (see MIL-HDBK-721C, “Definition of Terms for Reliability and Maintainability”). Other related references on this subject matter include MIL-HDBK-217, “Reliability Prediction of Electronic Equipment”; MIL-HDBK-883, “Test Methods and Procedures for Microelectronics”; and Nelson, “Accelerated Testing: Statistical Models, Test Plans, and Data Analyses”.
Indeed, the commercial viability of any product may be strongly determined by the product's reliability as potential users will not spend a significant sum of money on an unreliable product. The vital importance of product reliability spurred the emergence of Reliability Engineering as an engineering discipline that devotes itself to establishing, maintaining, and evolving the reliability of a product.
An exemplary useful reliability parameter is the product's failure rate (Failure In Time—FITS, failures per billion device hours) or the inverse measurement of Mean-Time-Between-Failures (MTBF). A failure is commonly defined as any event that prevents the product from performing its specified operations. A product's failure rate is the average rate at which the product will fail over its lifetime (e.g., 2 failures over 50 years). Conversely, the MTBF is total operating time divided by the number of failures over that period (e.g., 25 years/failure or 219,000 hours) which is generally regarded as the average length of time a user may reasonably expect the product to work before a failure occurs. Therefore, a primary design objective is to have a very low product failure rate or inversely a very high MTBF. For example, most commercial telephone switching equipment is designed for less than two hours of downtime in 40 years; undersea telephone systems are designed for less than three failures in 25 years; and personal computers are designed for a MTBF of at least 1,000,000 hours (assuming end-of-life replacement of component parts).
Therefore, an important reliability engineering procedure is to determine the MTBF for a product. Many reliability engineering methods determine MTBF by observing the product in the field during normal usage and by tracking the frequency of failures. However, this method is very costly and time-consuming and does not meet the practical commercial needs of a product manufacturer who wants to accurately predict the MTBF early in the development process, which is not possible with currently available methods. Early prediction of MTBF allows the product manufacturer to make any necessary design changes at a low cost to ensure a product deployment with a very low failure rate. Without early prediction of MTBF, a product manufacturer must rely on very time-consuming field use data (requiring months and years of field testing) which delays product deployment and adversely affects future sales. Alternatively, product quality may be reduced if deployment cannot wait for the field use data or the field use data is no longer useful once it is finally obtained, thereby increasing the risk of distributing defective products to the market.
To provide early prediction of MTBF, product manufacturers commonly use a procedure standardized by the military described as the “Bill of Materials” (BOM) approach (Standard MIL 217 and MIL-HNBK-217F Reliability Prediction of Electronic Equipment). The BOM approach theoretically determines the product's MTBF by using the MTBF of each product component. For example, each individual resistor, integrated circuit and other component in a system may have a known MTBF, and the MTBF of the complete product can be calculated using these values. However, the BOM approach is notoriously unreliable because of interactions between product components and other factors which may include, but are not limited to, manufacturing process control, end-use environment, and design validation and ruggedness.
Thus, there exists a need to provide an accurate and early estimation of a product's MTBF, enabling product manufacturers to deploy relatively defect-free products.